Suriname seabob shrimp CPUE analysis

1. Seabob shrimp fishery in Suriname

1.1 The Atlantic seabob shrimp Xiphopenaeus kroyeri

The Atlantic seabob shrimp Xiphopenaeus kroyeri (simply referred to as ‘seabob’) is a relatively small and short-lived shrimp species living in the shallow coastal waters on fine silt and muddy substrates, reaching commercial densities up to a depth of 33m in the Guianas. The species occurs along the Eastern coasts of North and South America, from 33oN to 33oZ. There are indications that the species reproduces year-round, with peaks in the rainy seasons (August and December). Observations indicate that seabob favours shallower, less saline near-shore waters in the dry season.

Atlantic seabob shrimp Xiphopenaeus kroyeri

1.2 The seabob shrimp fishery

The seabob industry uses ‘twin-rig’ shrimp trawlers which land the shrimp on ice to both Surinamese processing companies. The shrimp are peeled mechanically and frozen for export to Europa and the USA. Artisanal fishermen catch seabob in the river mouths using fyke nets (so called ‘Chinese seines’). Their catch is marketed fresh or dried for the local marked. This fishery lands about 500 tons per year, while the industrial fishery catches between 8,000 and 10,000 tons/year.

A seabob shrimp trawler off Suriname

2. Dynamics of the seabob trawl fishery (2002 - 2022)

2.1 Background on the dataset

The dataset contains monthly catch and effort data of the Atlantic seabob shrimp Xiphopenaeus kroyeri industrial trawl fishery off Suriname, from 2002 to 2022.

The data contains the following variables:

## [1] "year"        "month"       "catch_pw_kg" "effort_das"  "obs_cpue"   
## [6] "hcr_cpue"    "month_txt"

Catch weights are represented in processed (peeled and beheaded) weight, in kilograms, as received from the fishing industry. Effort is represented as days-at-sea, calculated by the formula departure date - arrival data + 1. Obs_cpue is the catch divided by effort, while hcr_cpue is the cpue index used in the fisheries’ harvest control rule (HCR). month and month_text represent the month by numbers (1-12) and text (Jan, Feb,..) respectivly.

Here is a glimpse of the dataset:

## # A tibble: 6 × 7
##    year month catch_pw_kg effort_das obs_cpue hcr_cpue month_txt
##   <dbl> <dbl>       <dbl>      <dbl>    <dbl>    <dbl> <ord>    
## 1  2002     1     325688.        388     838.     838. Jan      
## 2  2002     2     415042.        454     914.     914. Feb      
## 3  2002     3     428434.        421    1018.     992. Mar      
## 4  2002     4     405034.        435     930.     946. Apr      
## 5  2002     5     331839.        374     888.     902. May      
## 6  2002     6     299650.        380     788.     816. Jun

2.2 Some data exploration

First, lets look at yearly catch and effort between 2002 and 2022.

Now, let’s explore yearly catch-per-unit-effort (CPUE).

The graph below summarizes the CPUE per month, over the period 2002-2022.